CN115840145B

CN115840145B - Electrochemical parameter identification method, device, equipment and storage medium

Info

Publication number: CN115840145B
Application number: CN202211509917.5A
Authority: CN
Inventors: 张学思; 郝平超; 周志民; 杨洲; 严晓; 赵恩海
Original assignee: Shanghai MS Energy Storage Technology Co Ltd
Current assignee: Shanghai MS Energy Storage Technology Co Ltd
Priority date: 2022-11-29
Filing date: 2022-11-29
Publication date: 2023-07-25
Anticipated expiration: 2042-11-29
Also published as: CN115840145A

Abstract

The invention provides an electrochemical parameter identification method, an electrochemical parameter identification device, electrochemical parameter identification equipment and a storage medium, wherein the method comprises the following steps: acquiring continuous working data, pre-standing period data and post-standing period data of a battery to be tested; extracting a first characteristic value in working data, a second characteristic value in front standing period data and a third characteristic value in rear standing period data; determining a current solution of the electrochemical parameters based on a population algorithm, and using an electrochemical model to simulate and verify the current solution; the objective function of the population algorithm has an increasing regularization term; and under the condition that the current solution meets the requirements, taking the current solution as the electrochemical parameter of the battery to be tested. According to the scheme provided by the embodiment of the invention, under the condition that the working data is not full charge or full discharge data, the identification of the important chemical change period in the battery to be detected can be enhanced, and the electrochemical parameters can be accurately determined; better direction can be provided for the group iterative algorithm, and the convergence speed of the algorithm can be increased.

Description

Electrochemical parameter identification method, device, equipment and storage medium

Technical Field

The invention relates to the technical field of electrochemical models, in particular to an electrochemical parameter identification method, an electrochemical parameter identification device, electrochemical parameter identification equipment and a storage medium.

Background

The lithium battery is widely applied to energy storage power stations and new energy automobiles. In order to secure the safety of the lithium battery in long-term use, it is necessary to estimate the state of the lithium battery, such as the current charge (e.g., SOC) of the battery, the health (SOH) of the battery, etc. The internal state of the battery can be accurately simulated by an electrochemical modeling method, the precision of the electrochemical model is strongly related to the precision of parameters, and the electrochemical model parameters (electrochemical parameters for short) are mostly obtained by adopting a parameter identification method at present. Conventional electrochemical parameter identification generally utilizes various population algorithms (genetic algorithm, particle swarm algorithm, etc.), and obtains optimal parameter estimation by fitting a complete charge-discharge voltage curve and minimizing a voltage mean square residual error.

In order to ensure the accuracy of parameter identification, the battery is generally required to be fully charged and discharged; in the operation process of the real energy storage power station, the battery is not always fully charged and discharged. For example, for lithium iron phosphate batteries, if they are not fully discharged, the voltage data used for identification is in a flat plateau phase, and the electrochemical parameters cannot be accurately obtained by the existing parameter identification method.

Disclosure of Invention

In order to solve the existing technical problems, the embodiment of the invention provides an electrochemical parameter identification method, an electrochemical parameter identification device, electrochemical parameter identification equipment and a storage medium.

In a first aspect, an embodiment of the present invention provides an electrochemical parameter identification method, including:

acquiring continuous working condition data of a battery to be tested, wherein the continuous working condition data comprises working data, front standing period data and rear standing period data; the working data are data of the battery to be tested in a charging stage or a discharging stage, the data in the front standing stage are data of the battery to be tested in a standing stage before the working data, and the data in the rear standing stage are data of the battery to be tested in the standing stage after the working data;

extracting a first characteristic value at a first characteristic point in the working data, extracting a second characteristic value at a second characteristic point in the previous standing period data, and extracting a third characteristic value at a third characteristic point in the later standing period data;

determining a current solution of electrochemical parameters based on a population algorithm, and using an electrochemical model to simulate and verify the current solution; the objective function of the population algorithm has an increased regular term, and positive correlation relations are formed among the regular term, the first difference, the second difference and the third difference; the first difference is a difference between the first feature value and a first analog value at the first feature point determined by the electrochemical model, the second difference is a difference between the second feature value and a second analog value at the second feature point determined by the electrochemical model, and the third difference is a difference between the third feature value and a third analog value at the third feature point determined by the electrochemical model;

And under the condition that the current solution meets the requirement, taking the current solution as the electrochemical parameter of the battery to be tested.

In a possible implementation manner, the extracting a first feature value at a first feature point in the working data includes: taking the last data point in the working data as a first characteristic point, and taking the voltage at the first characteristic point as a first characteristic value; the first analog value is a voltage;

the extracting a second characteristic value at a second characteristic point in the previous standing period data comprises: taking a first data point in the previous standing period data as a second characteristic point, and taking the voltage at the second characteristic point as a second characteristic value; the second analog value is a voltage;

the extracting a third characteristic value at a third characteristic point in the later standing period data comprises: taking the last data point in the later standing period data as a third characteristic point, and taking the voltage at the third characteristic point as a third characteristic value; the third analog value is a voltage.

In one possible implementation manner, the minimum SOC in the working data is smaller than a first threshold value, the maximum SOC is larger than a second threshold value, the second threshold value is larger than the first threshold value, and the SOC corresponding to the step period of the battery to be tested is located between the first threshold value and the second threshold value;

The method further comprises the steps of:

determining the stage data in the voltage stage in the working data, and extracting a fourth characteristic value at a fourth characteristic point in the stage data, wherein the fourth characteristic point is a characteristic point at a step;

wherein, the regular term is also in positive correlation with the fourth difference; the fourth difference is a difference between the fourth eigenvalue and a fourth simulated value at the fourth eigenvalue determined by the electrochemical model.

In a possible implementation manner, the extracting a fourth feature value at a fourth feature point in the platform period data includes:

presetting a sliding window;

and sliding the sliding window, determining the difference value of two boundary voltages of the sliding window in the platform period data, and taking the data point which can represent step abrupt change in the sliding window as a fourth characteristic point under the condition that the difference value of the two boundary voltages is larger than a preset threshold value.

taking the time at the fourth characteristic point as a fourth characteristic value; the fourth simulation value is the time determined by the electrochemical model to reach the fourth feature point at the step.

In one possible implementation, the regularization term satisfies:

wherein W represents the regularization term, V ₁ The first characteristic value is represented by a first value,representing the first analog value, V ₂ Representing said second characteristic value, +_>Representing the second analog value, V ₃ Representing the third characteristic value, +_>Representing the third analog value, T ₁ Representing the fourth characteristic value, +_>Representing the fourth analog value; a, a ₁ ,a ₂ ,a ₃ ,a ₄ Is the corresponding coefficient; the first characteristic value, the first analog value, the second characteristic value, the second analog value, the third characteristic value and the third analog value are all voltages.

In one possible implementation, the determining the current solution of the electrochemical parameter based on the population algorithm includes:

determining the electrochemical parameter range of the type corresponding to the battery to be tested;

based on a population algorithm, a current solution of the electrochemical parameter is determined over the electrochemical parameter range.

In a second aspect, an embodiment of the present invention further provides an electrochemical parameter identification apparatus, including:

the device comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring continuous working condition data of a battery to be tested, and the continuous working condition data comprise working data, front standing period data and rear standing period data; the working data are data of the battery to be tested in a charging stage or a discharging stage, the data in the front standing stage are data of the battery to be tested in a standing stage before the working data, and the data in the rear standing stage are data of the battery to be tested in the standing stage after the working data;

The extraction module is used for extracting a first characteristic value at a first characteristic point in the working data, extracting a second characteristic value at a second characteristic point in the previous standing period data and extracting a third characteristic value at a third characteristic point in the later standing period data;

the simulation module is used for determining a current solution of the electrochemical parameters based on a population algorithm, and using an electrochemical model to simulate and verify the current solution; the objective function of the population algorithm has an increased regular term, and positive correlation relations are formed among the regular term, the first difference, the second difference and the third difference; the first difference is a difference between the first feature value and a first analog value at the first feature point determined by the electrochemical model, the second difference is a difference between the second feature value and a second analog value at the second feature point determined by the electrochemical model, and the third difference is a difference between the third feature value and a third analog value at the third feature point determined by the electrochemical model;

and the determining module is used for taking the current solution as the electrochemical parameter of the battery to be tested under the condition that the current solution meets the requirement.

In a third aspect, an embodiment of the present invention provides an electrochemical parameter identification apparatus, including a processor and a memory, where the memory stores a computer program, where the processor executes the computer program stored in the memory, and the computer program when executed by the processor implements the electrochemical parameter identification method according to the first aspect.

In a fourth aspect, an embodiment of the present invention further provides a computer readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the electrochemical parameter identification method according to the first aspect.

In a fifth aspect, the present application further provides a computer program product, which comprises a computer program, when the computer program is executed, can implement the electrochemical parameter identification method according to the first aspect or any one of the possible design manners of the first aspect.

According to the electrochemical parameter identification method, the device, the equipment and the storage medium provided by the embodiment of the invention, the selected working condition data comprises working data of a charging stage or a discharging stage, pre-standing period data before the working data and post-standing period data after the working data, characteristic values of corresponding characteristic points are extracted from the working data, the pre-standing period data and the post-standing period data, and the characteristic values are compared with simulation values determined by electrochemical model simulation, so that an objective function of a group algorithm is optimized. According to the electrochemical parameter identification method, parameter identification is carried out by using working condition data in a standing period, and identification of important chemical change periods in a battery to be tested can be enhanced under the condition that the working data are not full charge or full discharge data, so that electrochemical parameters can be accurately determined; and the difference between the characteristic value and the analog value is used as a regular term to be added into an objective function of the group algorithm, so that a better direction can be provided for the group iterative algorithm, and the convergence speed of the algorithm can be increased.

Drawings

In order to more clearly describe the embodiments of the present invention or the technical solutions in the background art, the following description will describe the drawings that are required to be used in the embodiments of the present invention or the background art.

FIG. 1 is a flow chart of an electrochemical parameter identification method according to an embodiment of the present invention;

FIG. 2 shows a voltage curve of selected data of a certain working condition in the electrochemical parameter identification method according to the embodiment of the present invention;

fig. 3 is a schematic diagram showing feature points selected in the electrochemical parameter identification method according to the embodiment of the present invention;

FIG. 4 is a flowchart illustrating another method for identifying electrochemical parameters according to an embodiment of the present invention;

FIG. 5 is a schematic diagram illustrating an electrochemical parameter identification device according to an embodiment of the present invention;

fig. 6 is a schematic structural diagram of an apparatus for electrochemical parameter identification according to an embodiment of the present invention.

Detailed Description

Embodiments of the present invention will be described below with reference to the accompanying drawings in the embodiments of the present invention.

Fig. 1 shows a flowchart of an electrochemical parameter identification method according to an embodiment of the present invention, where electrochemical parameters of a battery can be determined relatively accurately by using working condition data of a standing period even if the battery is not fully charged or fully discharged. As shown in fig. 1, the method includes:

Step 101: acquiring continuous working condition data of a battery to be tested, wherein the working condition data comprise working data, front standing period data and rear standing period data; the working data are data of the battery to be tested in a charging stage or a discharging stage, the data in the front standing stage are data of the battery to be tested in the standing stage before the working data, and the data in the rear standing stage are data of the battery to be tested in the standing stage after the working data.

In the embodiment of the invention, when the electrochemical parameter of a certain battery needs to be determined, the battery is used as the battery to be measured, for example, the battery of the energy storage power station is used as the battery to be measured. Wherein electrochemical parameters refer to parameters describing an electrochemical model of the cell under test. For example, the electrochemical model may be a Pseudo-two-dimensional (P2D) model, and the electrochemical parameters include solid-liquid diffusion coefficient, solid-liquid conductivity, thickness of the separator, particle radius, cathode-anode transfer coefficient, and the like, which are not described in detail in this embodiment. The battery to be tested can be a lithium battery.

In the normal working process of the battery to be tested, the current, voltage and other data of the battery to be tested can be collected to form working condition data of the battery to be tested; for example, the current and the voltage corresponding to different time stamps of the battery to be measured can be obtained through measurement of a Battery Management System (BMS) and the like, so that the working condition data of the battery to be measured are formed. The operating mode data is generally discrete data, which includes the current, voltage, etc. of the battery to be measured at various points in time.

When the electrochemical parameters of the battery to be measured need to be determined, continuous data of the battery to be measured in a charging stage or a discharging stage, namely working condition data of the battery to be measured in charging or discharging, are determined, and the data are called as 'working data' for convenience of description. The working data is a continuous segment of data, which can be working condition data of the battery to be tested when fully charged or fully discharged, or working condition data of the battery to be tested when not fully charged or not fully discharged, and the embodiment is not limited to the working condition data; in practice, the selected working data is typically either non-full-charge or non-full-discharge. The working data is complete charging or discharging working condition data, namely the battery to be tested is not charged or discharged in the adjacent time period before or after the working data, and the battery to be tested stands; correspondingly, working condition data of the battery to be measured in a standing period exist before and after the working data, the working condition data of the battery to be measured in the standing period before the working data is called as 'pre-standing period data', and the working condition data of the battery to be measured in the standing period after the working data is called as 'post-standing period data'. The front standing period data, the working data and the rear standing period data are working condition data of the battery to be tested in corresponding stages, and the working condition data are continuous working condition data.

For example, when the working condition data of the battery to be measured needs to be selected, if the battery to be measured is firstly stood for a period of time (in a pre-standing period), then is charged or discharged (in a charging period or a discharging period), and is continuously stood for a period of time (in a post-standing period) after the charging or discharging is finished, the working condition data in the period of time can be used as the required working condition data. Fig. 2 shows working condition data (time on abscissa and voltage on ordinate in fig. 2) of a certain battery to be measured, wherein the working condition data includes data of the battery to be measured in a previous rest period (i.e. data of the previous rest period), data of the battery to be measured in a discharge period (i.e. working data) and data of the battery to be measured in a later rest period (i.e. data of the later rest period), and the previous rest period, the discharge period and the later rest period are continuous time periods, and accordingly, the selected working condition data are continuous.

Step 102: and extracting a first characteristic value at a first characteristic point in the working data, extracting a second characteristic value at a second characteristic point in the data of the previous standing period, and extracting a third characteristic value at a third characteristic point in the data of the later standing period.

In the embodiment of the invention, the characteristic points are respectively extracted at different stages in the working condition data.

Specifically, a certain data point in the working data is selected as a characteristic point, namely a first characteristic point; the first feature point also has a corresponding feature value, i.e., a first feature value. Wherein the first feature point may be a critical data point in the working data. Alternatively, since the voltage at the end of charge and discharge can generally reflect electrochemical parameters such as lithium ion concentration, diffusion coefficient of the anode, particle radius, and the like, which have an important influence on capacity estimation, the first feature point may be the last data point in the corresponding stage, that is, the last data point in the working data is the first feature point. For example, if the working data is data of a charging stage, the last data point of the charging stage is taken as a first characteristic point, and the first characteristic point is generally the data point corresponding to the maximum voltage; if the working data is the data of the discharge stage, the last data point of the discharge stage is taken as a first characteristic point, and the first characteristic point is generally the data point corresponding to the minimum voltage. As shown in fig. 3, the battery to be measured is discharged at the point a, then enters a later standing period, and the voltage of the battery to be measured is increased due to a relaxation effect until the voltage is stable; this point a may be considered as the first feature point at this time.

The voltage of the battery to be measured during the rest period can generally reflect the lithium ion concentration range, for example, the voltage of the battery during the previous rest period can reflect the initial lithium ion concentration range; and, there is also a voltage drop between the rest period and the normal working period (charging period or discharging period) of the battery to be measured, which can reflect the polarization state of the battery to be measured. Therefore, corresponding characteristic points can be respectively extracted from the battery to be measured in the front standing period and the rear standing period. Wherein, a certain characteristic point in the data of the previous standing period is taken as a second characteristic point, and the characteristic value of the second characteristic point is called as a second characteristic value; a certain characteristic point in the post-stationary phase data is taken as a third characteristic point, and the characteristic value of the third characteristic point is called a third characteristic value.

The second characteristic point and the third characteristic point are characteristic points when the battery to be tested is stable. Alternatively, the first data point in the previous rest period data can be used as the second characteristic point; for example, the data point at which the operating condition data starts is taken as the second feature point. The last data point in the later standing period data can be used as a third characteristic point; for example, the data point at the end of the operating mode data is taken as the third feature point. As shown in fig. 3: the point B is a data point of the working condition data start, and the point B is a second characteristic point; the point C is a data point at the end of the working condition data, and may be a third feature point.

Step 103: determining a current solution of the electrochemical parameters based on a population algorithm, and using an electrochemical model to simulate and verify the current solution; the objective function of the population algorithm has an increased regular term, and the regular term is in positive correlation with the first difference, the second difference and the third difference; the first difference is a difference between the first feature value and a first analog value at a first feature point determined by the electrochemical model, the second difference is a difference between the second feature value and a second analog value at a second feature point determined by the electrochemical model, and the third difference is a difference between a third feature value and a third analog value at a third feature point determined by the electrochemical model.

Step 104: and under the condition that the current solution meets the requirements, taking the current solution as the electrochemical parameter of the battery to be tested.

In the embodiment of the invention, the parameter identification is still performed based on the group algorithm, and the step 103 is basically the same as the conventional parameter identification process based on the group algorithm, except that the embodiment of the invention optimizes the objective function of the group algorithm based on the determined first characteristic value, second characteristic value and third characteristic value, thereby providing a better direction for iteration of the group algorithm, increasing the convergence speed of the algorithm, and obtaining a high-precision identification result when the selected working condition data is not full charge or not full discharge.

The population algorithm is an algorithm for searching the optimal solution of the problem through a multi-round iteration mode, for example, the population algorithm can be a genetic algorithm, a particle swarm optimization algorithm, a cuckoo search algorithm and the like, and the embodiment is not limited to the above. After each round of processing, the current optimal solution, namely the current optimal electrochemical parameter, can be determined, then a corresponding electrochemical model is constructed by the current optimal electrochemical parameter, the difference between the data obtained by simulation of the electrochemical model and the actual working condition data is determined, and the objective function of the population algorithm represents the difference between the two. If the difference between the two is smaller, the current optimal electrochemical parameter can be used as the finally determined electrochemical parameter; if the difference between the two is large, the electrochemical parameters are further optimized based on a population algorithm, the optimal electrochemical parameters of the next round are determined, and the process is repeated until the difference between the two is small.

As described above, when the parameter identification is performed based on the population algorithm, the objective function of the population algorithm represents the difference between the data obtained by the electrochemical model simulation and the actual operating mode data, and in general, the objective function represents the voltage difference between the two, for example, the objective function is the mean square error of the global voltage.

In the embodiment of the invention, on the basis of the objective function of the group algorithm, a regular term is added for the objective function so as to realize the optimization of the objective function. Specifically, for the first feature point, the second feature point and the third feature point selected in the embodiment of the present invention, actual feature values, that is, the first feature value, the second feature value and the third feature value, may be determined based on the working condition data; when the simulation is performed by using the electrochemical model, the characteristic value at each characteristic point may be determined, and the characteristic value determined by the simulation is referred to as "the simulated value", and accordingly, the simulated value at the first characteristic point (i.e., the first simulated value), the simulated value at the second characteristic point (i.e., the second simulated value), and the simulated value at the third characteristic point (i.e., the third simulated value) may be obtained by using the electrochemical model in a simulation.

The regular term comprises a difference (namely a first difference) between the first characteristic value and the first analog value, a difference (namely a second difference) between the second characteristic value and the second analog value, and a difference (namely a third difference) between the third characteristic value and the third analog value, and the regular term has positive correlation with the first difference, the second difference and the third difference; that is, the larger the first difference is, the larger the value corresponding to the regular term is, as are the second difference and the third difference . Wherein the absolute value of the difference between the characteristic value and the analog value may be taken as the corresponding difference. For example, if the first characteristic value is V ₁ The first analog value isThe first difference can be expressed as +.>And adding the function of the regular term on the basis of the original objective function to serve as a new objective function, and finally determining a current solution meeting the requirements by using a group algorithm, wherein the current solution can serve as the electrochemical parameter of the battery to be tested. For example, a current solution is satisfactory when the objective function is less than a preset threshold.

The simulation conditions of the electrochemical model in simulation are consistent with the actual working conditions of the working condition data. For example, the working condition data is that the battery to be tested is firstly kept stand for 5min, then is charged for 1h by constant current of current I, and is then kept stand for 10min; correspondingly, the simulation conditions of the electrochemical model are that the electrochemical model is firstly kept stand for 5min, then is charged for 1h by constant current of current I, and is then kept stand for 10min.

According to the electrochemical parameter identification method provided by the embodiment of the invention, the selected working condition data comprises working data of a charging stage or a discharging stage, front standing period data before the working data and rear standing period data after the working data, characteristic values of corresponding characteristic points are extracted from the working data, the front standing period data and the rear standing period data, and the characteristic values are compared with simulation values determined by electrochemical model simulation, so that an objective function of a group algorithm is optimized. According to the electrochemical parameter identification method, parameter identification is carried out by using working condition data in a standing period, and identification of important chemical change periods in a battery to be tested can be enhanced under the condition that the working data are not full charge or full discharge data, so that electrochemical parameters can be accurately determined; and the difference between the characteristic value and the analog value is used as a regular term to be added into an objective function of the group algorithm, so that a better direction can be provided for the group iterative algorithm, and the convergence speed of the algorithm can be increased.

Optionally, one or more steps may occur in most lithium batteries in the stage, that is, one or more steps are also present in the stage, and the step corresponds to an important chemical reaction period. Wherein, in order to ensure that the working data comprise a step period in the selected working condition data, the data volume of the working data is required to meet the requirement; specifically, the minimum SOC in the working data is smaller than a first threshold, the maximum SOC is larger than a second threshold, the second threshold is larger than the first threshold, and the SOC corresponding to the step period is located between the first threshold and the second threshold.

In the embodiment of the invention, for different types of batteries to be tested, the SOC (state of charge) when the battery is stepped is generally stable, so that when working condition data is selected, whether the battery contains a stepped period or not can be basically determined based on the SOC range of the working condition data; in the embodiment of the invention, a first threshold value and a second threshold value are preset, and if the minimum SOC in a certain section of working condition data is smaller than the first threshold value and the maximum SOC is larger than the second threshold value, the section of working condition data can be considered as required working data. The first threshold value is not larger than the corresponding SOC of the battery to be tested when the platform phase appears at the beginning, and the second threshold value is not smaller than the corresponding SOC of the battery to be tested when the last platform phase appears at the end.

For example, the plateau SOC of lithium iron phosphate batteries is typically between about 97% and about 18%, with the first step occurring near 40% SOC; thus, the first threshold may be less than 30% and the second threshold may be greater than 50% such that the operating data contains data with an SOC equal to 40%. For example, the SOC length of the working data (i.e., the difference between the maximum SOC and the minimum SOC) may be 20% to 70%, for example, the SOC length of the working data may be 50% to 60%, as long as the working data is ensured to contain a step period. For example, for a charge condition, charge condition data for an SOC from 20% to 90% may be selected as the operating data, which would typically include the step. The magnitudes of the first threshold and the second threshold may be specific to the actual situation.

In the case that the working data includes data of a step period, the electrochemical parameter identification method further includes the following step A1:

step A1: and determining the stage data in the voltage stage in the working data, and extracting a fourth characteristic value at a fourth characteristic point in the stage data, wherein the fourth characteristic point is a characteristic point at a step. Wherein, the regular term is also in positive correlation with the fourth difference; the fourth difference is a difference between the fourth feature value and a fourth simulated value at a fourth feature point determined by the electrochemical model.

In the embodiment of the invention, the data in the platform stage, namely the platform stage data, in the working data is firstly determined, and the platform stage data is relatively stable data. Then, determining a step period from the platform period data, and selecting a characteristic point, namely a fourth characteristic point, from the step period; accordingly, the fourth feature point is located at the step. As shown in fig. 2, most of the discharge phase is a plateau, and there is an obvious step in the plateau, where the step is located, that is, the step, and the fourth feature point may be selected in the step. Referring to fig. 3, a point D is located in the step period, and the point D may be a fourth characteristic point.

In the embodiment of the invention, the first threshold value and the second threshold value have the function of ensuring that the working data contains data in a step period, when the working data is selected, the SOC of the working data can not be determined, and under the condition that the working data is long enough (namely, the charging time is long enough or the discharging time is long enough), the minimum SOC in the working data can be considered to be smaller than the first threshold value, and the maximum SOC is larger than the second threshold value, and the working data contains data in the step period. And under the condition that the working data is long enough, the platform period data can be roughly extracted, namely, the platform period data can be ensured to have steps, namely, the platform period data comprises the data of the step period. For example, the battery to be tested is an energy storage power station, and the energy storage power station has a standing period and a shorter standing period; at the moment, the middle part data can be directly extracted from the working condition data (including the front standing period data, the working data and the rear standing period data) of the energy storage power station according to the percentage, and the part data can be used as the platform period data. For example, the portion of the operating condition data from 5% to 80% is taken as plateau data, and typically includes a step.

Alternatively, a sliding window may be utilized to position a step into the landing stage. The step A1 'extracting the fourth characteristic value at the fourth characteristic point in the platform period data' comprises the following steps A11-A12:

step A11: a sliding window is preset.

Step A12: and sliding the sliding window, determining the difference value of two boundary voltages of the sliding window in the platform period data, and taking the data point capable of representing the step mutation in the sliding window as a fourth characteristic point under the condition that the difference value of the two boundary voltages is larger than a preset threshold value.

In the embodiment of the invention, a sliding window with proper size is set, and platform period data is traversed based on the sliding window; the sliding window is provided with two boundaries, when traversing the data in the platform period, the two boundaries correspond to corresponding data points, each data point has corresponding voltage, and whether the sliding window is positioned at the step or not is determined based on the difference value of the two boundary voltages, so that the step in the platform period can be positioned. The length of the sliding window can be matched with the length of the step period. The length of the sliding window can be set based on human experience; for example, the length of the sliding window may be approximately one tenth of an hour divided by the current multiplying power, and may be adjusted according to circumstances.

Specifically, a preset threshold value is preset, which may represent the magnitude of the voltage change in the step period. If the difference between the two boundary voltages of the sliding window is larger than the preset threshold value when the sliding window slides to a certain position, the difference between the two ends of the sliding window is large enough, and the sliding window comprises steps; otherwise, the sliding window is only in a non-stepped position during the plateau phase. The embodiment of the invention can simply and quickly position the step in the platform by utilizing the sliding window, and has accurate positioning and small processing capacity.

When the sliding window is located at the step, a fourth feature point can be selected from the sliding window, and in the embodiment of the invention, a data point capable of representing the step abrupt change is selected as the fourth feature point. For example, the data point with the largest voltage change rate in the sliding window is taken as a fourth characteristic point; alternatively, the data point at the center position of the sliding window may be simply set as the fourth feature point, which is not limited in this embodiment.

The preset threshold value may be a preset fixed value; if the difference between the two boundary voltages is greater than a preset threshold when the sliding window is at a plurality of positions, for example, a plurality of step periods exist, the feature points can be extracted from each sliding window. Or, since the partial type battery has only one step period (or only one step period is needed), in order to improve the applicability and avoid erroneous judgment, the fourth feature point may be selected from the sliding window when the difference between the two boundary voltages is the largest. For example, if the difference between the two boundary voltages is the largest, it may be considered that "the difference between the two boundary voltages is larger than a preset threshold value", which is equivalent to a threshold value based on the actual situation, that is, the preset threshold value is a value slightly smaller than the maximum value of the difference between the two boundary voltages.

In addition, similar to the first feature point, the second feature point and the third feature point, the fourth feature point is also used for correcting the objective function of the population algorithm, that is, the regularization term added by the objective function is also related to the fourth feature point. In the iterative process of the population algorithm, the simulation value at the fourth characteristic point, namely a fourth simulation value, can be determined based on the electrochemical model; and setting the difference between the fourth characteristic value and the fourth analog value as a fourth difference, wherein the regular term and the fourth difference are also in positive correlation.

According to the embodiment of the invention, the objective function of the group algorithm is optimized by combining the characteristic points of the working end time (such as the charging end time or the discharging end time), the front standing period, the rear standing period and the step period, and the influence of the electrochemical parameter on the standing period, the step period and the working end time can be comprehensively considered, so that the determined electrochemical parameter is more consistent with the real condition of the battery to be tested, and the electrochemical parameter is more accurate.

Optionally, when simulating by using the electrochemical model, the simulation condition is the same as the real working condition of the working condition data, so that the timestamps corresponding to the second characteristic point and the third characteristic point are fixed no matter the real working condition data or the data obtained by simulation; if the data point at the end of the operation is the first feature point, the time stamp of the first feature point is also fixed. Therefore, the time stamp can be utilized to position the first characteristic point, the second characteristic point and the third characteristic point, and corresponding first characteristic value, second characteristic value and third characteristic value are further determined based on the working condition data; and determining corresponding first analog value, second analog value and third analog value based on the simulation of the electrochemical model. Specifically, the first, second and third characteristic values may be voltages, and correspondingly, the first, second and third analog values are voltages.

Similarly, the fourth characteristic value and the fourth analog value may also be voltages; for example, a time stamp corresponding to a fourth feature point in the real working condition data is used as a positioning reference, and a voltage of a data point corresponding to the time stamp in the simulated data is used as a fourth simulation value. The objective function is optimized based on the voltage differences at the four feature points.

Furthermore, the inventors found that if the fourth characteristic value and the fourth analog value are voltages, there may be an overall error of the voltage platform, such as early stage of the voltage platform obtained by simulation; in the embodiment of the invention, the time stamp of the actual working condition data and the time stamp of the simulated data reaching the step may be different, and the embodiment uses the characteristic to perform time fitting on the fourth characteristic point at the step instead of voltage fitting, namely the fourth characteristic point is time, and correspondingly, the fourth simulation point is time.

Specifically, the extracting the fourth feature value "at the fourth feature point in the platform period data in the step A1" includes:

step B1: taking the time at the fourth characteristic point as a fourth characteristic value; the fourth simulation value is the time determined by the electrochemical model to reach a fourth feature point located at the step.

In the embodiment of the invention, when the fourth characteristic point in the working condition data is positioned, the time of the fourth characteristic point is taken as a fourth characteristic value. Correspondingly, after simulation data are simulated based on an electrochemical model, a step in the simulation data is required to be positioned, and then the time of a characteristic point (also a fourth characteristic point) at the step is determined, wherein the time is a fourth simulation value; that is, the fourth feature point in the embodiment of the present invention refers to a feature point located at a step, the time stamp of which may not be fixed. For example, the time to reach the step in the simulation data may be determined based on the similar manner of the steps a11-a12, which will not be described in detail in this embodiment.

In the embodiment of the invention, the fourth characteristic point of the step period is subjected to time fitting, namely the time difference of the step period is subjected to fitting, and the time difference is used as a measurement standard, so that the integral deviation can be effectively avoided, the rapid convergence is facilitated, and the relatively accurate electrochemical parameter can be obtained.

For example, the objective function of the conventional population algorithm is F ₀ In the embodiment of the invention, the objective function F ₀ The addition of the regularization term W, i.e., the objective function F of the population algorithm in the embodiment of the present invention, can be expressed as: f=f ₀ +W. If the fourth characteristic value and the fourth analog value are both time, the regularization term W may satisfy the following formula (1):

wherein W represents a regular term, V ₁ A first characteristic value is indicated and a second characteristic value is indicated,representing the first analog value, V ₂ A second characteristic value is indicated and is used to represent,representing a second analog value, V ₃ Representing a third characteristic value, ">Representing a third analog value，T ₁ Representing the fourth characteristic value,/->Representing a fourth analog value; a, a ₁ ,a ₂ ,a ₃ ,a ₄ Is the corresponding coefficient. The first characteristic value, the first analog value, the second characteristic value, the second analog value, the third characteristic value and the third analog value are all voltages.

One flow of the electrochemical parameter identification method is described in detail below by way of one embodiment. Referring to fig. 4, the electrochemical parameter identification method includes steps 401-409:

step 401: and acquiring continuous working condition data of the battery to be tested.

The working condition data comprise front standing period data, working data and rear standing period data, and the working data comprise a step period.

Step 402: taking the last data point in the working data as a first characteristic point, taking the first data point in the previous standing period data as a second characteristic point, and taking the last data point in the later standing period data as a third characteristic point; and the voltages at the first characteristic point, the second characteristic point and the third characteristic point are used as the first characteristic value, the second characteristic value and the third characteristic value.

In the embodiment of the invention, V is ₁ Representing the first characteristic value, V ₂ Representing the second characteristic value, V ₃ Representing a third characteristic value.

Step 403: and positioning a step in the working data, selecting a fourth characteristic point at the step, and taking the time at the fourth characteristic point as a fourth characteristic value.

For example, the fourth feature point may be located based on the above steps a11-a12, and the corresponding time, i.e. the fourth feature value, may be determined. The embodiment uses T ₁ Representing the fourth characteristic value.

Step 404: determining the electrochemical parameter range of the type corresponding to the battery to be tested; and, setting an objective function of the population algorithm.

For example, the electrochemical parameter range of the corresponding type of the battery to be identified can be determined by experimental testing, searching documents and the like.

Moreover, the regular term W is added on the basis of the mean square error MSE (V) of the global voltage in the embodiment of the present invention, that is, the objective function of the group algorithm in the embodiment of the present invention may be expressed as: f=mse (V) +w. The regular term W specifically satisfies the above expression (1).

Step 405: the current optimal solution is determined based on a population algorithm.

The current optimal solution is the currently determined optimal electrochemical parameter. The process of determining the current optimal solution based on the population algorithm is the prior art, and is not described in detail herein.

Step 406: and establishing an electrochemical model based on the current optimal solution, and performing simulation based on the electrochemical model to determine current simulation data.

Step 407: and positioning corresponding first, second, third and fourth characteristic points in the simulation data, taking voltages at the first, second and third characteristic points as corresponding first, second and third simulation values, and taking time at the fourth characteristic point as a fourth simulation value.

Wherein, byRepresenting a first analog value,/>Representing a second analog value,/>Representing a third analog value, ">Representing a fourth analog value.

Step 408: and judging whether the value of the objective function is smaller than a preset value, if so, continuing to step 409, otherwise, continuing to step 405.

The preset value is a preset value used for indicating whether the iterative process of the group algorithm is finished or not. Substituting the first characteristic value, the first analog value, the second characteristic value, the second analog value, the third characteristic value, the third analog value, the fourth characteristic value and the fourth analog value into the objective function to determine the value of the objective function. If the value of the objective function is smaller than the preset value, the data obtained by current electrochemical parameter simulation is basically consistent with the real working condition data, and the iteration can be ended at the moment; otherwise, it is indicated that the current electrochemical parameters cannot accurately represent the parameters of the battery to be measured, and the optimization needs to be continued, i.e. step 405 needs to be performed again.

Step 409: and taking the current optimal solution as the electrochemical parameter of the battery to be tested.

According to the electrochemical parameter identification method provided by the embodiment of the invention, parameter identification is performed by using the working condition data in the standing period, and the identification of the important chemical change period in the battery to be tested can be enhanced under the condition that the working data is not full charge or full discharge data, so that the electrochemical parameter can be accurately determined; and the difference between the characteristic value and the analog value is used as a regular term to be added into an objective function of the group algorithm, so that a better direction can be provided for the group iterative algorithm, and the convergence speed of the algorithm can be increased. The objective function of the group algorithm is optimized by combining the characteristic points of the working end time (such as the charging end time or the discharging end time), the front and rear standing periods and the step period, and the influence of the electrochemical parameters on the standing period, the step period and the working end time can be comprehensively considered, so that the determined electrochemical parameters are more consistent with the actual situation of the battery to be measured, and the electrochemical parameters are more accurate. And performing time fitting on the fourth characteristic point of the step period, namely fitting on the time difference of the step period, and taking the time difference as a measurement standard, so that the integral deviation can be effectively avoided.

The electrochemical parameter identification method provided by the embodiment of the invention is described in detail above, and the method can also be implemented by a corresponding device, and the electrochemical parameter identification device provided by the embodiment of the invention is described in detail below.

Fig. 5 is a schematic structural diagram of an electrochemical parameter identification device according to an embodiment of the invention. As shown in fig. 5, the electrochemical parameter identification device includes:

the acquisition module 51 is configured to acquire continuous working condition data of the battery to be tested, where the continuous working condition data includes working data, pre-standing period data and post-standing period data; the working data are data of the battery to be tested in a charging stage or a discharging stage, the data in the front standing stage are data of the battery to be tested in a standing stage before the working data, and the data in the rear standing stage are data of the battery to be tested in the standing stage after the working data;

an extracting module 52, configured to extract a first feature value at a first feature point in the working data, extract a second feature value at a second feature point in the previous standing period data, and extract a third feature value at a third feature point in the later standing period data;

the simulation module 53 is configured to determine a current solution of the electrochemical parameter based on a population algorithm, and perform simulation using an electrochemical model to verify the current solution; the objective function of the population algorithm has an increased regular term, and positive correlation relations are formed among the regular term, the first difference, the second difference and the third difference; the first difference is a difference between the first feature value and a first analog value at the first feature point determined by the electrochemical model, the second difference is a difference between the second feature value and a second analog value at the second feature point determined by the electrochemical model, and the third difference is a difference between the third feature value and a third analog value at the third feature point determined by the electrochemical model;

And the determining module 54 is configured to take the current solution as the electrochemical parameter of the battery to be tested if the current solution meets the requirement.

In one possible implementation of the present invention,

the extracting module 52 extracts a first feature value at a first feature point in the working data, including: taking the last data point in the working data as a first characteristic point, and taking the voltage at the first characteristic point as a first characteristic value; the first analog value is a voltage;

the extracting module 52 extracts a second feature value at a second feature point in the previous rest period data, including: taking a first data point in the previous standing period data as a second characteristic point, and taking the voltage at the second characteristic point as a second characteristic value; the second analog value is a voltage;

the extracting module 52 extracts a third feature value at a third feature point in the post-rest period data, including: taking the last data point in the later standing period data as a third characteristic point, and taking the voltage at the third characteristic point as a third characteristic value; the third analog value is a voltage.

In one possible implementation, the minimum SOC in the working data is less than a first threshold, and the maximum SOC is greater than a second threshold, the second threshold being greater than the first threshold; the extraction module 52 is further configured to:

In one possible implementation, the extracting module 52 extracts a fourth feature value at a fourth feature point in the platform period data, including:

presetting a sliding window;

In one possible implementation, the regularization term satisfies:

In one possible implementation, the simulation module 53 determines a current solution of the electrochemical parameter based on a population algorithm, including:

It should be noted that, when the electrochemical parameter identification apparatus provided in the above embodiment implements the corresponding function, only the division of the above functional modules is used for illustration, and in practical application, the above functional allocation may be implemented by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules, so as to implement all or part of the functions described above. In addition, the electrochemical parameter identification apparatus and the electrochemical parameter identification method provided in the foregoing embodiments belong to the same concept, and detailed implementation processes thereof are shown in the method embodiments, which are not repeated herein.

According to one aspect of the present application, the present embodiment also provides a computer program product comprising a computer program comprising program code for performing the method shown in the flowchart. In such an embodiment, the computer program may be downloaded and installed from a network through a communication section. When the computer program is executed by the processor, the electrochemical parameter identification method provided by the embodiment of the application is executed.

In addition, the embodiment of the invention also provides electrochemical parameter identification equipment, which comprises a processor and a memory, wherein the memory stores a computer program, the processor can execute the computer program stored in the memory, and when the computer program is executed by the processor, the electrochemical parameter identification method provided by any embodiment can be realized.

For example, FIG. 6 illustrates an electrochemical parameter identification device provided by an embodiment of the present invention, the device comprising a bus 1110, a processor 1120, a transceiver 1130, a bus interface 1140, a memory 1150, and a user interface 1160.

In an embodiment of the present invention, the apparatus further includes: a computer program stored in the memory 1150 and executable on the processor 1120, which when executed by the processor 1120, performs the processes of the electrochemical parameter identification method embodiments described above.

A transceiver 1130 for receiving and transmitting data under the control of the processor 1120.

In an embodiment of the invention, represented by bus 1110, bus 1110 may include any number of interconnected buses and bridges, with bus 1110 connecting various circuits, including one or more processors, represented by processor 1120, and memory, represented by memory 1150.

Bus 1110 represents one or more of any of several types of bus structures, including a memory bus and a memory controller, a peripheral bus, an accelerated graphics port (Accelerate Graphical Port, AGP), a processor, or a local bus using any of a variety of bus architectures. By way of example, and not limitation, such an architecture includes: industry standard architecture (Industry Standard Architecture, ISA) bus, micro channel architecture (Micro Channel Architecture, MCA) bus, enhanced ISA (EISA) bus, video electronics standards association (Video Electronics Standards Association, VESA) bus, peripheral component interconnect (Peripheral Component Interconnect, PCI) bus.

Processor 1120 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method embodiments may be implemented by instructions in the form of integrated logic circuits in hardware or software in a processor. The processor includes: general purpose processors, central processing units (Central Processing Unit, CPU), network processors (Network Processor, NP), digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field Programmable Gate Array, FPGA), complex programmable logic devices (Complex Programmable Logic Device, CPLD), programmable logic arrays (Programmable Logic Array, PLA), micro control units (Microcontroller Unit, MCU) or other programmable logic devices, discrete gates, transistor logic devices, discrete hardware components. The methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. For example, the processor may be a single-core processor or a multi-core processor, and the processor may be integrated on a single chip or located on multiple different chips.

The processor 1120 may be a microprocessor or any conventional processor. The steps of the method disclosed in connection with the embodiments of the present invention may be performed directly by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software modules may be located in a random access Memory (Random Access Memory, RAM), flash Memory (Flash Memory), read-Only Memory (ROM), programmable ROM (PROM), erasable Programmable ROM (EPROM), registers, and so forth, as are known in the art. The readable storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method.

Bus 1110 may also connect together various other circuits such as peripheral devices, voltage regulators, or power management circuits, bus interface 1140 providing an interface between bus 1110 and transceiver 1130, all of which are well known in the art. Accordingly, the embodiments of the present invention will not be further described.

The transceiver 1130 may be one element or a plurality of elements, such as a plurality of receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. For example: the transceiver 1130 receives external data from other devices, and the transceiver 1130 is configured to transmit the data processed by the processor 1120 to the other devices. Depending on the nature of the computer system, a user interface 1160 may also be provided, for example: touch screen, physical keyboard, display, mouse, speaker, microphone, trackball, joystick, stylus.

It should be appreciated that in embodiments of the present invention, the memory 1150 may further comprise memory located remotely from the processor 1120, such remotely located memory being connectable to a server through a network. One or more portions of the above-described networks may be an ad hoc network (ad hoc network), an intranet, an extranet (extranet), a Virtual Private Network (VPN), a Local Area Network (LAN), a Wireless Local Area Network (WLAN), a Wide Area Network (WAN), a Wireless Wide Area Network (WWAN), a Metropolitan Area Network (MAN), the Internet (Internet), a Public Switched Telephone Network (PSTN), a plain old telephone service network (POTS), a cellular telephone network, a wireless fidelity (Wi-Fi) network, and a combination of two or more of the above-described networks. For example, the cellular telephone network and wireless network may be a global system for mobile communications (GSM) system, a Code Division Multiple Access (CDMA) system, a Worldwide Interoperability for Microwave Access (WiMAX) system, a General Packet Radio Service (GPRS) system, a Wideband Code Division Multiple Access (WCDMA) system, a Long Term Evolution (LTE) system, an LTE Frequency Division Duplex (FDD) system, an LTE Time Division Duplex (TDD) system, a long term evolution-advanced (LTE-a) system, a Universal Mobile Telecommunications (UMTS) system, an enhanced mobile broadband (Enhance Mobile Broadband, embbb) system, a mass machine type communication (massive Machine Type of Communication, mctc) system, an ultra reliable low latency communication (Ultra Reliable Low Latency Communications, uirllc) system, and the like.

It should be appreciated that the memory 1150 in embodiments of the present invention may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. Wherein the nonvolatile memory includes: read-Only Memory (ROM), programmable ROM (PROM), erasable Programmable EPROM (EPROM), electrically Erasable EPROM (EEPROM), or Flash Memory (Flash Memory).

The volatile memory includes: random access memory (Random Access Memory, RAM) which acts as an external cache. By way of example, and not limitation, many forms of RAM are available, such as: static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (ddr SDRAM), enhanced SDRAM (Enhanced SDRAM), synchronous DRAM (SLDRAM), and Direct RAM (DRAM). Memory 1150 described in embodiments of the present invention includes, but is not limited to, the above and any other suitable types of memory.

In an embodiment of the invention, memory 1150 stores the following elements of operating system 1151 and application programs 1152: an executable module, a data structure, or a subset thereof, or an extended set thereof.

Specifically, the operating system 1151 includes various system programs, such as: a framework layer, a core library layer, a driving layer and the like, which are used for realizing various basic services and processing tasks based on hardware. The applications 1152 include various applications such as: a Media Player (Media Player), a Browser (Browser) for implementing various application services. A program for implementing the method of the embodiment of the present invention may be included in the application 1152. The application 1152 includes: applets, objects, components, logic, data structures, and other computer system executable instructions that perform particular tasks or implement particular abstract data types.

In addition, the embodiment of the invention further provides a computer readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the processes of the above-mentioned electrochemical parameter identification method embodiment are implemented, and the same technical effects can be achieved, so that repetition is avoided, and no further description is provided herein.

The computer-readable storage medium includes: persistent and non-persistent, removable and non-removable media are tangible devices that may retain and store instructions for use by an instruction execution device. The computer-readable storage medium includes: electronic storage, magnetic storage, optical storage, electromagnetic storage, semiconductor storage, and any suitable combination of the foregoing. The computer-readable storage medium includes: phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), non-volatile random access memory (NVRAM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disk read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassette storage, magnetic tape disk storage or other magnetic storage devices, memory sticks, mechanical coding (e.g., punch cards or bump structures in grooves with instructions recorded thereon), or any other non-transmission medium that may be used to store information that may be accessed by a computing device. In accordance with the definition in the present embodiments, the computer-readable storage medium does not include a transitory signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a pulse of light passing through a fiber optic cable), or an electrical signal transmitted through a wire.

In the several embodiments provided herein, it should be understood that the disclosed apparatus, devices, and methods may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, e.g., the division of the modules or units is merely a logical functional division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. In addition, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices, or elements, or may be an electrical, mechanical, or other form of connection.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one position, or may be distributed over a plurality of network units. Some or all of the units can be selected according to actual needs to solve the problem to be solved by the scheme of the embodiment of the invention.

In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the embodiments of the present invention is essentially or partly contributing to the prior art, or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (including: a personal computer, a server, a data center or other network device) to perform all or part of the steps of the method according to the embodiments of the present invention. And the storage medium includes various media as exemplified above that can store program codes.

In the description of the embodiments of the present invention, those skilled in the art should appreciate that the embodiments of the present invention may be implemented as a method, an apparatus, a device, and a storage medium. Thus, embodiments of the present invention may be embodied in the following forms: complete hardware, complete software (including firmware, resident software, micro-code, etc.), a combination of hardware and software. Furthermore, in some embodiments, embodiments of the invention may also be implemented in the form of a computer program product in one or more computer-readable storage media having computer program code embodied therein.

Any combination of one or more computer-readable storage media may be employed by the computer-readable storage media described above. The computer-readable storage medium includes: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of the computer readable storage medium include the following: portable computer diskette, hard disk, random Access Memory (RAM), read-only Memory (ROM), erasable programmable read-only Memory (EPROM), flash Memory (Flash Memory), optical fiber, compact disc read-only Memory (CD-ROM), optical storage device, magnetic storage device, or any combination thereof. In embodiments of the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, device.

The computer program code embodied in the computer readable storage medium may be transmitted using any appropriate medium, including: wireless, wire, fiber optic cable, radio Frequency (RF), or any suitable combination thereof.

Computer program code for carrying out operations of embodiments of the present invention may be written in assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, integrated circuit configuration data, or in one or more programming languages, including an object oriented programming language such as: java, smalltalk, C ++, also include conventional procedural programming languages, such as: c language or similar programming language. The computer program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of remote computers, the remote computers may be connected via any sort of network, including: a Local Area Network (LAN) or a Wide Area Network (WAN), which may be connected to the user's computer or to an external computer.

The embodiments of the present invention describe the provided methods, apparatuses, devices through flowcharts and/or block diagrams.

It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions. These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

These computer readable program instructions may also be stored in a computer readable storage medium that can cause a computer or other programmable data processing apparatus to function in a particular manner. Thus, instructions stored in a computer-readable storage medium produce an instruction means which implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

The foregoing is merely a specific implementation of the embodiment of the present invention, but the protection scope of the embodiment of the present invention is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the embodiment of the present invention, and the changes or substitutions are covered by the protection scope of the embodiment of the present invention. Therefore, the protection scope of the embodiments of the present invention shall be subject to the protection scope of the claims.

Claims

1. An electrochemical parameter identification method, comprising:

2. The method of claim 1, wherein the step of determining the position of the substrate comprises,

the extracting a first characteristic value at a first characteristic point in the working data comprises: taking the last data point in the working data as a first characteristic point, and taking the voltage at the first characteristic point as a first characteristic value; the first analog value is a voltage;

3. The method of claim 1, wherein a minimum SOC in the operating data is less than a first threshold, a maximum SOC is greater than a second threshold, the second threshold is greater than the first threshold, and a SOC corresponding to a step period of the battery under test is between the first threshold and the second threshold;

the method further comprises the steps of:

4. A method according to claim 3, wherein said extracting a fourth feature value at a fourth feature point in the platform phase data comprises:

presetting a sliding window;

5. A method according to claim 3, wherein said extracting a fourth feature value at a fourth feature point in the platform phase data comprises:

6. The method of claim 5, wherein the regularization term satisfies:

7. The method of claim 1, wherein the determining the current solution of the electrochemical parameter based on the population algorithm comprises:

8. An electrochemical parameter identification device, comprising:

9. Electrochemical parameter identification device comprising a processor and a memory, the memory storing a computer program, characterized in that the processor executes the computer program stored in the memory to implement the electrochemical parameter identification method according to any one of claims 1 to 7.

10. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the electrochemical parameter identification method according to any one of claims 1 to 7.